



import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin



@require_phonemizer
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Optional[int]								=     WavaVecaPhonemeCTCTokenizer
				A_   :     List[Any]								=     False


				def __UpperCamelCase						(							self      :  int       )		->     int:
											super().setUp()

											A      						=    (
											    '<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '
											    'ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '
											    'ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '
											    'oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '
											    'pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '
											    'yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '
											    'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
											    'ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '
											    'ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '
											    'uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '
											    'ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '
											    'ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '
											    'ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'
											).split(' '       )
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )

											A      						=    {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write(json.dumps(__UpperCamelCase       ) + '\n'       )


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  int						, __UpperCamelCase      :  str=False						, __UpperCamelCase      :  Optional[int]=20						, __UpperCamelCase      :  Any=5       )		->     Tuple[str, list]:
											A      						=    [(i, tokenizer.decode([i]						, clean_up_tokenization_spaces=__UpperCamelCase       )) for i in range(len(__UpperCamelCase       )       )]
											A      						=    list(filter(lambda __UpperCamelCase       : [t[0]] == tokenizer.encode(t[1]						, do_phonemize=__UpperCamelCase       )						, __UpperCamelCase       )       )
											if max_length is not None and len(__UpperCamelCase       ) > max_length:
																		A      						=    toks[:max_length]
											if min_length is not None and len(__UpperCamelCase       ) < min_length and len(__UpperCamelCase       ) > 0:
																		while len(__UpperCamelCase       ) < min_length:
																									A      						=    toks + toks
        # toks_str = [t[1] for t in toks]
											A      						=    [t[0] for t in toks]

											# Ensure consistency
											A      						=    tokenizer.decode(__UpperCamelCase						, clean_up_tokenization_spaces=__UpperCamelCase       )
											if " " not in output_txt and len(__UpperCamelCase       ) > 1:
																		A      						=    (
																		    tokenizer.decode([toks_ids[0]]						, clean_up_tokenization_spaces=__UpperCamelCase       )
																		    + ' '
																		    + tokenizer.decode(toks_ids[1:]						, clean_up_tokenization_spaces=__UpperCamelCase       )
																		)
											if with_prefix_space:
																		A      						=    ' ' + output_txt
											A      						=    tokenizer.encode(__UpperCamelCase						, add_special_tokens=__UpperCamelCase       )
											return output_txt, output_ids


				def __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  Dict       )		->     str:
											kwargs.update(self.special_tokens_map       )
											return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Any       )		->     List[Any]:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )

											# check adding a single token
											tokenizer.add_tokens('xxx'       )
											A      						=    tokenizer('m xxx ɪ'						, do_phonemize=__UpperCamelCase       ).input_ids
											self.assertEqual(__UpperCamelCase						, [13, 392, 17]       )  # xxx should be last token

											tokenizer.add_tokens(['aaa', 'bbb', 'ccc']       )
											A      						=    tokenizer('m aaa ɪ ccc'						, do_phonemize=__UpperCamelCase       ).input_ids
											self.assertEqual(__UpperCamelCase						, [13, 393, 17, 395]       )  # aaa and ccc should be after xxx and 2 after aaa

											A      						=    tokenizer('maɪ c'						, do_phonemize=__UpperCamelCase       ).input_ids
											self.assertEqual(__UpperCamelCase						, [3, 200]       )  # mai should be <unk> (=3)


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )
											self.assertEqual(__UpperCamelCase						, 'h ə l oʊ h aʊ ɑːɹ j uː'       )


				def __UpperCamelCase						(							self      :  int       )		->     int:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )
											self.assertEqual(tokenizer(__UpperCamelCase       ).input_ids						, tokenizer(__UpperCamelCase						, do_phonemize=__UpperCamelCase       ).input_ids       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Union[str, Any]:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )
											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )

											A      						=    tokenizer.decode(tokenizer(__UpperCamelCase       ).input_ids       )

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )

											A      						=    [
											    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
											    [24, 22, 5, 24, 22, 5, 77],
											]
											A      						=    tokenizer.decode(sample_ids[0]       )
											A      						=    tokenizer.batch_decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, batch_tokens[0]       )
											self.assertEqual(__UpperCamelCase						, ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']       )


				def __UpperCamelCase						(							self      :  str       )		->     Optional[int]:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )
											self.assertEqual(__UpperCamelCase						, 'h ə l oʊ | h aʊ | ɑːɹ | j uː |'       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Union[str, Any]:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )
											self.assertEqual(tokenizer(__UpperCamelCase       ).input_ids						, tokenizer(__UpperCamelCase						, do_phonemize=__UpperCamelCase       ).input_ids       )


				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

											# fmt: off
											A      						=    [
											    [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
											    [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
											]
											# fmt: on

											# decode with word_del_token filter
											A      						=    tokenizer.decode(sample_ids[0]       )
											A      						=    tokenizer.batch_decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, batch_tokens[0]       )
											self.assertEqual(__UpperCamelCase						, ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']       )

											# decode with no word_del_token filter
											A      						=    tokenizer.decode(sample_ids[0]						, filter_word_delimiter_token=__UpperCamelCase       )
											A      						=    tokenizer.batch_decode(__UpperCamelCase						, filter_word_delimiter_token=__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, batch_tokens[0]       )
											self.assertEqual(__UpperCamelCase						, ['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ']       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )

											A      						=    tokenizer.decode(tokenizer(__UpperCamelCase       ).input_ids						, filter_word_delimiter_token=__UpperCamelCase       )

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Any       )		->     Optional[int]:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

											A      						=    'Hello how are you'
											A      						=    tokenizer.phonemize(__UpperCamelCase						, phonemizer_lang='en-us'       )

											A      						=    tokenizer.decode(tokenizer(__UpperCamelCase       ).input_ids						, filter_word_delimiter_token=__UpperCamelCase       )

											self.assertEqual(' '.join([p.strip() for p in phonemes.split(' |'       )]       ).strip()						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Union[str, Any]:
											A      						=    self.tokenizer_class.from_pretrained(
											    'facebook/wav2vec2-lv-60-espeak-cv-ft'						, word_delimiter_token=__UpperCamelCase       )
											A      						=    'Hello how are you'

											A      						=    tokenizer(__UpperCamelCase						, phonemizer_lang='en-us'       ).input_ids
											A      						=    tokenizer(__UpperCamelCase						, phonemizer_lang='fr-fr'       ).input_ids

											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )

											self.assertEqual(__UpperCamelCase						, 'h ə l oʊ h aʊ ɑːɹ j uː'       )
											self.assertEqual(__UpperCamelCase						, 'ɛ l o h aʊ a ʁ j u'       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )
											A      						=    'Hello how Are you'
											A      						=    'hello how are you'

											A      						=    tokenizer(__UpperCamelCase       ).input_ids
											A      						=    tokenizer(__UpperCamelCase       ).input_ids

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[str]:
											A      						=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'       )
											tokenizer.add_tokens(['!', '?']       )
											tokenizer.add_special_tokens({'cls_token': '$$$'}       )

											# fmt: off
											A      						=    [
											    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
											    [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
											]
											# fmt: on

											A      						=    tokenizer.batch_decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$']       )


				@staticmethod
				def __UpperCamelCase						(							__UpperCamelCase      :  Dict						, __UpperCamelCase      :  Dict       )		->     Optional[Any]:
											A      						=    [d[key] for d in offsets]
											return retrieved_list


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											A      						=    self.get_tokenizer(word_delimiter_token='|'       )
											tokenizer.add_tokens('|'       )

											# fmt: off
											# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
											A      						=    [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
											# fmt: on

											A      						=    tokenizer.decode(__UpperCamelCase						, output_char_offsets=__UpperCamelCase						, filter_word_delimiter_token=__UpperCamelCase       )
											# check Wav2Vec2CTCTokenizerOutput keys for char
											self.assertEqual(len(outputs.keys()       )						, 2       )
											self.assertTrue('text' in outputs       )
											self.assertTrue('char_offsets' in outputs       )
											self.assertTrue(isinstance(__UpperCamelCase						, __UpperCamelCase       )       )

											# check that order of chars is correct and identical for both outputs
											self.assertEqual(' '.join(self.get_from_offsets(outputs['char_offsets']						, 'char'       )       )						, outputs.text       )
											self.assertListEqual(
											    self.get_from_offsets(outputs['char_offsets']						, 'char'       )						, ['k', 's', 'ɾ', 'ɾ', '|', 'ɾ', 'l', '|', 'ɭʲ']       )

											# check that offsets are actually correct for char
											# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
											# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
											self.assertListEqual(
											    self.get_from_offsets(outputs['char_offsets']						, 'start_offset'       )						, [0, 1, 4, 7, 9, 11, 12, 15, 16]       )
											self.assertListEqual(
											    self.get_from_offsets(outputs['char_offsets']						, 'end_offset'       )						, [1, 4, 6, 9, 10, 12, 15, 16, 17]       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											A      						=    self.get_tokenizer(word_delimiter_token='|'       )

											def check_list_tuples_equal(__UpperCamelCase      :  Any						, __UpperCamelCase      :  Optional[Any]       ):
																		self.assertTrue(isinstance(__UpperCamelCase						, __UpperCamelCase       )       )
																		self.assertTrue(isinstance(outputs_list[0]						, __UpperCamelCase       )       )

																		# transform list to ModelOutput
																		A      						=    WavaVecaPhonemeCTCTokenizerOutput(
																		    {k: [d[k] for d in outputs_list] for k in outputs_list[0]}       )

																		self.assertListEqual(outputs_batch['text']						, outputs_batch_a['text']       )

																		def recursive_check(__UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Optional[int]       ):
																									if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																																[recursive_check(__UpperCamelCase						, __UpperCamelCase       ) for la, la in zip(__UpperCamelCase						, __UpperCamelCase       )]
																									self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )

																		if "char_offsets" in outputs_batch:
																									recursive_check(outputs_batch['char_offsets']						, outputs_batch_a['char_offsets']       )

        # fmt: off
											A      						=    [
											    [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
											    [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
											]
											# fmt: on

											# We assume that `decode` works as expected. All we will check now is
											# the output type is correct and the output is identical to `decode`

											# char
											A      						=    tokenizer.batch_decode(__UpperCamelCase						, output_char_offsets=__UpperCamelCase       )
											A      						=    [tokenizer.decode(__UpperCamelCase						, output_char_offsets=__UpperCamelCase       ) for ids in sample_ids]
											check_list_tuples_equal(__UpperCamelCase						, __UpperCamelCase       )


				@unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'       )
				def __UpperCamelCase						(							self      :  List[str]       )		->     int:
											pass


				@unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'       )
				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											pass


				@unittest.skip('encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency'       )
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											pass


				@unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing'       )
				def __UpperCamelCase						(							self      :  Dict       )		->     List[str]:
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     Tuple:
											A      						=    self.get_tokenizers(do_lower_case=__UpperCamelCase       )
											for tokenizer in tokenizers:
																		with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
																									A      						=    tokenizer.vocab_size
																									A      						=    len(__UpperCamelCase       )

																									self.assertNotEqual(__UpperCamelCase						, 0       )

																									# We usually have added tokens from the start in tests because our vocab fixtures are
																									# smaller than the original vocabs - let's not assert this
																									# self.assertEqual(vocab_size, all_size)

																									A      						=    ['aaaaa bbbbbb', 'cccccccccdddddddd']
																									A      						=    tokenizer.add_tokens(__UpperCamelCase       )
																									A      						=    tokenizer.vocab_size
																									A      						=    len(__UpperCamelCase       )

																									self.assertNotEqual(__UpperCamelCase						, 0       )
																									self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
																									self.assertEqual(__UpperCamelCase						, len(__UpperCamelCase       )       )
																									self.assertEqual(__UpperCamelCase						, all_size + len(__UpperCamelCase       )       )

																									A      						=    tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'						, add_special_tokens=__UpperCamelCase       )

																									self.assertGreaterEqual(len(__UpperCamelCase       )						, 4       )
																									self.assertGreater(tokens[0]						, tokenizer.vocab_size - 1       )
																									self.assertGreater(tokens[-3]						, tokenizer.vocab_size - 1       )

																									A      						=    {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
																									A      						=    tokenizer.add_special_tokens(__UpperCamelCase       )
																									A      						=    tokenizer.vocab_size
																									A      						=    len(__UpperCamelCase       )

																									self.assertNotEqual(__UpperCamelCase						, 0       )
																									self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
																									self.assertEqual(__UpperCamelCase						, len(__UpperCamelCase       )       )
																									self.assertEqual(__UpperCamelCase						, all_size_a + len(__UpperCamelCase       )       )

																									A      						=    tokenizer.encode(
																									    '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'						, add_special_tokens=__UpperCamelCase       )

																									self.assertGreaterEqual(len(__UpperCamelCase       )						, 6       )
																									self.assertGreater(tokens[0]						, tokenizer.vocab_size - 1       )
																									self.assertGreater(tokens[0]						, tokens[1]       )
																									self.assertGreater(tokens[-3]						, tokenizer.vocab_size - 1       )
																									self.assertGreater(tokens[-3]						, tokens[-4]       )
																									self.assertEqual(tokens[0]						, tokenizer.eos_token_id       )
																									self.assertEqual(tokens[-3]						, tokenizer.pad_token_id       )


				@unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.'       )
				def __UpperCamelCase						(							self      :  Dict       )		->     Union[str, Any]:
											pass


				@unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.'       )
				def __UpperCamelCase						(							self      :  List[Any]       )		->     int:
											pass


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
											# is not the case for Wav2Vec2PhonemeCTCTokenizer.
											A      						=    self.get_tokenizers(fast=__UpperCamelCase						, do_lower_case=__UpperCamelCase       )
											for tokenizer in tokenizers:
																		with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
																									A      						=    ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
																									A      						=    tokenizer.convert_tokens_to_string(__UpperCamelCase       )

																									self.assertIsInstance(output['text']						, __UpperCamelCase       )







"""simple docstring"""
from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFTransfoXLForSequenceClassification,
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )



class        UpperCamelCase__ :







    """simple docstring"""







    def __init__(		self :	int				,    UpperCamelCase_ :	Union[str, Any]				,    ):





           '''simple docstring'''

           __magic_name__      				=    parent
           __magic_name__      				=    1_3
           __magic_name__      				=    7
           __magic_name__      				=    3_0
           __magic_name__      				=    self.seq_length + self.mem_len
           __magic_name__      				=    1_5
           __magic_name__      				=    True
           __magic_name__      				=    True
           __magic_name__      				=    9_9
           __magic_name__      				=    [1_0, 5_0, 8_0]
           __magic_name__      				=    3_2
           __magic_name__      				=    3_2
           __magic_name__      				=    4
           __magic_name__      				=    8
           __magic_name__      				=    1_2_8
           __magic_name__      				=    2
           __magic_name__      				=    2
           __magic_name__      				=    None
           __magic_name__      				=    1
           __magic_name__      				=    0
           __magic_name__      				=    3
           __magic_name__      				=    self.vocab_size - 1
           __magic_name__      				=    0.01







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )
           __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

           __magic_name__      				=    None
           if self.use_labels:
                  __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

           __magic_name__      				=    TransfoXLConfig(
               vocab_size=self.vocab_size				,    mem_len=self.mem_len				,    clamp_len=self.clamp_len				,    cutoffs=self.cutoffs				,    d_model=self.hidden_size				,    d_embed=self.d_embed				,    n_head=self.num_attention_heads				,    d_head=self.d_head				,    d_inner=self.d_inner				,    div_val=self.div_val				,    n_layer=self.num_hidden_layers				,    eos_token_id=self.eos_token_id				,    pad_token_id=self.vocab_size - 1				,    init_range=self.init_range				,    num_labels=self.num_labels				,    )

           return (config, input_ids_a, input_ids_a, lm_labels)







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           random.seed(self.seed )
           tf.random.set_seed(self.seed )







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	int				,    UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           __magic_name__      				=    TFTransfoXLModel(UpperCamelCase_ )

           __magic_name__				,  __magic_name__      				=    model(UpperCamelCase_ ).to_tuple()

           __magic_name__      				=    {'input_ids': input_ids_a, 'mems': mems_a}

           __magic_name__				,  __magic_name__      				=    model(UpperCamelCase_ ).to_tuple()

           self.parent.assertEqual(hidden_states_a.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )
           self.parent.assertEqual(hidden_states_a.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]				,    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,    )
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]				,    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,    )







    def 				a__			(		self :	Optional[int]				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    TFTransfoXLLMHeadModel(UpperCamelCase_ )

           __magic_name__				,  __magic_name__      				=    model(UpperCamelCase_ ).to_tuple()

           __magic_name__      				=    {'input_ids': input_ids_a, 'labels': lm_labels}
           __magic_name__				,  __magic_name__      				=    model(UpperCamelCase_ ).to_tuple()

           __magic_name__				,  __magic_name__      				=    model([input_ids_a, mems_a] ).to_tuple()

           __magic_name__      				=    {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

           __magic_name__				,  __magic_name__      				=    model(UpperCamelCase_ ).to_tuple()

           self.parent.assertEqual(lm_logits_a.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]				,    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,    )

           self.parent.assertEqual(lm_logits_a.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )
           self.parent.assertListEqual(
               [mem.shape for mem in mems_a]				,    [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,    )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    TFTransfoXLForSequenceClassification(UpperCamelCase_ )
           __magic_name__      				=    model(UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels) )







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    self.prepare_config_and_inputs()
           ((__magic_name__)				,  (__magic_name__)				,  (__magic_name__)				,  (__magic_name__))      				=    config_and_inputs
           __magic_name__      				=    {'input_ids': input_ids_a}
           return config, inputs_dict



@require_tf
class        UpperCamelCase__ (				a_							,	a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    __UpperCAmelCase           = () if is_tf_available() else ()
    __UpperCAmelCase           = (
        {
            """feature-extraction""": TFTransfoXLModel,
            """text-classification""": TFTransfoXLForSequenceClassification,
            """text-generation""": TFTransfoXLLMHeadModel,
            """zero-shot""": TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False







    def 				a__			(		self :	int				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	Tuple ):





           '''simple docstring'''

           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                  # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
                  # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
                  # tokenizer.
                  return True

           return False







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    TFTransfoXLModelTester(self )
           __magic_name__      				=    ConfigTester(self				,    config_class=UpperCamelCase_				,    d_embed=3_7 )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.config_tester.run_common_tests()







    def 				a__			(		self :	int ):





           '''simple docstring'''

           self.model_tester.set_seed()
           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_ )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           self.model_tester.set_seed()
           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_ )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_ )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()
           __magic_name__      				=    [TFTransfoXLForSequenceClassification]

           for model_class in self.all_model_classes:
                  __magic_name__      				=    model_class(UpperCamelCase_ )
                  assert isinstance(model.get_input_embeddings()				,    tf.keras.layers.Layer )
                  if model_class in list_other_models_with_output_ebd:
                         __magic_name__      				=    model.get_output_embeddings()
                         assert isinstance(UpperCamelCase_				,    tf.keras.layers.Layer )
                         __magic_name__      				=    model.get_bias()
                         assert name is None
                  else:
                         __magic_name__      				=    model.get_output_embeddings()
                         assert x is None
                         __magic_name__      				=    model.get_bias()
                         assert name is None







    def 				a__			(		self :	int ):





           '''simple docstring'''

           pass







    @slow
    def 				a__			(		self :	str ):





           '''simple docstring'''

           for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __magic_name__      				=    TFTransfoXLModel.from_pretrained(UpperCamelCase_ )
                  self.assertIsNotNone(UpperCamelCase_ )







    @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.' )
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           pass



@require_tf
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @unittest.skip('Skip test until #12651 is resolved.' )
    @slow
    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103' )
           # fmt: off
           __magic_name__      				=    tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]				,    dtype=tf.intaa )  # noqa: E231
           # fmt: on
           #  In 1991 , the remains of Russian Tsar Nicholas II and his family
           #  ( except for Alexei and Maria ) are discovered .
           #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
           #  remainder of the story . 1883 Western Siberia ,
           #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
           #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
           #  father initially slaps him for making such an accusation , Rasputin watches as the
           #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
           #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
           #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

           # fmt: off
           __magic_name__      				=    [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
           # fmt: on
           #  In 1991, the remains of Russian Tsar Nicholas II and his family (
           #  except for Alexei and Maria ) are discovered. The voice of young son,
           #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
           #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
           #  and a group of men to perform magic. Rasputin has a vision and
           #  denounces one of the men as a horse thief. Although his father initially
           #  slaps him for making such an accusation, Rasputin watches as the man
           #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
           #  of the Virgin Mary, prompting him to become a priest.
           #  Rasputin quickly becomes famous, with people, even a bishop, begging for
           #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
           # Nicholas II and his family were discovered. The voice of <unk> young son,
           # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

           __magic_name__      				=    model.generate(UpperCamelCase_				,    max_length=2_0_0				,    do_sample=UpperCamelCase_ )
           self.assertListEqual(output_ids[0].numpy().tolist()				,    UpperCamelCase_ )